Next, we compared the SVM model with RBF and the linear kernel functions to ... of this study was to compare the classic machine learning model SVM with GBLUP and BayesR. In previous studies, most ...

Abstract: Support vector machine ... learning theory (SLT). SVM is powerful for the problem with small samples, non linear and high dimension. A multi-class SVM classifier is applied to predict the ...

We hear about applications of machine learning on a ... K-Nearest Neighbors, and Support Vector Machine (SVM). You can also use ensemble methods (combinations of models), such as Random Forest ...

Conclusion: In conclusion, the combination of WGCNA and SVM holds potential in biomarker screening and diagnostic model construction for Parkinson’s disease. • This study was the first to utilize a ...

Abstract: This paper proposes a framework based on Federated Learning (FL) for power line fault diagnosis. By constructing Random Forest (RF), Support Vector Machine (SVM) and Linear Regression (LR) ...

Support Vector Machines (SVMs) are a powerful and versatile supervised machine learning algorithm primarily used for classification ... The core principle behind SVM is to identify the optimal ...

Machine learning ... network (GNN) models. Random forest models make estimations by building decision-making trees based on several binary decisions for the inputs. SVM models provide lines ...

Machine learning ... has to be transparent. These models include linear and decision/regression tree models. On the other hand, black-box models, such as deep-learning (deep neural network ...